Comparing Acoustic-based Approaches for Alzheimer's Disease Detection

• URL: http://arxiv.org/abs/2106.01555v1
• Date: Thu, 3 Jun 2021 02:44:40 GMT
• Title: Comparing Acoustic-based Approaches for Alzheimer's Disease Detection
• Authors: Aparna Balagopalan, Jekaterina Novikova
• Abstract summary: We study the performance and generalizability of three approaches for AD detection from speech on the recent ADReSSo challenge dataset. We find that while feature-based approaches have a higher precision, classification approaches relying on the combination of embeddings and features prove to have a higher, and more balanced performance across multiple metrics of performance.
• Score: 8.360862198568967
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we study the performance and generalizability of three approaches for AD detection from speech on the recent ADReSSo challenge dataset: 1) using conventional acoustic features 2) using novel pre-trained acoustic embeddings 3) combining acoustic features and embeddings. We find that while feature-based approaches have a higher precision, classification approaches relying on the combination of embeddings and features prove to have a higher, and more balanced performance across multiple metrics of performance. Our best model, using such a combined approach, outperforms the acoustic baseline in the challenge by 2.8\%.

Related papers

• Acoustic Model Optimization over Multiple Data Sources: Merging and Valuation [13.009945735929445]
  We propose a novel paradigm to solve salient problems plaguing the Automatic Speech Recognition field. In the first stage, multiple acoustic models are trained based upon different subsets of the complete speech data. In the second stage, two novel algorithms are utilized to generate a high-quality acoustic model.
  arXiv  Detail & Related papers  (2024-10-21T03:48:23Z)
• MLCA-AVSR: Multi-Layer Cross Attention Fusion based Audio-Visual Speech Recognition [62.89464258519723]
  We propose a multi-layer cross-attention fusion based AVSR approach that promotes representation of each modality by fusing them at different levels of audio/visual encoders. Our proposed approach surpasses the first-place system, establishing a new SOTA cpCER of 29.13% on this dataset.
  arXiv  Detail & Related papers  (2024-01-07T08:59:32Z)
• Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection [69.53626024091076]
  Alzheimer's disease (AD) is particularly prominent in older adults. Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations. This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features.
  arXiv  Detail & Related papers  (2023-03-14T16:03:28Z)
• A Comparative Study on Speaker-attributed Automatic Speech Recognition in Multi-party Meetings [53.120885867427305]
  Three approaches are evaluated for speaker-attributed automatic speech recognition (SA-ASR) in a meeting scenario. The WD-SOT approach achieves 10.7% relative reduction on averaged speaker-dependent character error rate (SD-CER) The TS-ASR approach also outperforms the FD-SOT approach and brings 16.5% relative average SD-CER reduction.
  arXiv  Detail & Related papers  (2022-03-31T06:39:14Z)
• Self-attention fusion for audiovisual emotion recognition with incomplete data [103.70855797025689]
  We consider the problem of multimodal data analysis with a use case of audiovisual emotion recognition. We propose an architecture capable of learning from raw data and describe three variants of it with distinct modality fusion mechanisms.
  arXiv  Detail & Related papers  (2022-01-26T18:04:29Z)
• Generalizing Face Forgery Detection with High-frequency Features [63.33397573649408]
  Current CNN-based detectors tend to overfit to method-specific color textures and thus fail to generalize. We propose to utilize the high-frequency noises for face forgery detection. The first is the multi-scale high-frequency feature extraction module that extracts high-frequency noises at multiple scales. The second is the residual-guided spatial attention module that guides the low-level RGB feature extractor to concentrate more on forgery traces from a new perspective.
  arXiv  Detail & Related papers  (2021-03-23T08:19:21Z)
• To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection [17.99855227184379]
  Natural language processing and machine learning provide promising techniques for reliably detecting Alzheimer's disease (AD) We compare and contrast the performance of two such approaches for AD detection on the recent ADReSS challenge dataset. We observe that fine-tuned BERT models, given the relative importance of linguistics in cognitive impairment detection, outperform feature-based approaches on the AD detection task.
  arXiv  Detail & Related papers  (2020-07-26T04:50:47Z)
• Capturing scattered discriminative information using a deep architecture in acoustic scene classification [49.86640645460706]
  In this study, we investigate various methods to capture discriminative information and simultaneously mitigate the overfitting problem. We adopt a max feature map method to replace conventional non-linear activations in a deep neural network. Two data augment methods and two deep architecture modules are further explored to reduce overfitting and sustain the system's discriminative power.
  arXiv  Detail & Related papers  (2020-07-09T08:32:06Z)
• Exploration of Audio Quality Assessment and Anomaly Localisation Using Attention Models [37.60722440434528]
  In this paper, a novel model for audio quality assessment is proposed by jointly using bidirectional long short-term memory and an attention mechanism. The former is to mimic a human auditory perception ability to learn information from a recording, and the latter is to further discriminate interferences from desired signals by highlighting target related features. To evaluate our proposed approach, the TIMIT dataset is used and augmented by mixing with various natural sounds.
  arXiv  Detail & Related papers  (2020-05-16T17:54:07Z)
• Robust Speaker Recognition Using Speech Enhancement And Attention Model [37.33388614967888]
  Instead of individually processing speech enhancement and speaker recognition, the two modules are integrated into one framework by a joint optimisation using deep neural networks. To increase robustness against noise, a multi-stage attention mechanism is employed to highlight the speaker related features learned from context information in time and frequency domain. The obtained results show that the proposed approach using speech enhancement and multi-stage attention models outperforms two strong baselines not using them in most acoustic conditions in our experiments.
  arXiv  Detail & Related papers  (2020-01-14T20:03:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.